ORIGINAL_ARTICLE
A smart channel estimation approach for LTE systems using PSO algorithm
This study focuses on developing an effective channel estimation approach using swarm Intelligence. The Orthogonal Frequency Division Multiplexing ( OFDM) is a modulation technique used to counter transmission channel frequency selection to reach high data rate without disruption. The theory of OFDM is to gain prominence in the field of wireless communication. OFDM is combined with the transmitter and receiver antenna to amplify the variety gain and improve system capacity on selective time and frequency channels, resulting in a Multiple Input Multiple Output ( MIMO) pattern. The most commonly used channel estimation techniques are the Least Square (LS) approaches and Minimum Mean Square Error (MMSE) approaches. In LS, the estimation1process is simple but the problem is that the square error has a high mean. The MMSE is better in Low SNR than in LS, but its main problem is its high computational complexity. A unique method is proposed in this research study that combines LS and MMSE to overcome the aforementioned problems. Upgraded PSO is introduced in this study to select the best channel. This proposed approach is also more efficient and requires less time compared to other techniques to estimate the best channel.
http://aotp.fabad-ihe.ac.ir/article_119879_9560d7dfc935d1999a78cc215a4e1aed.pdf
2020-12-01
1
13
10.22121/aotp.2020.242757.1037
Channel Estimation
improved PSO
LS
MMSE
OFDM
Siraj
Pathan
sirajpathan404@gmail.com
1
PhD Research Scholar at Amity University Jaipur &amp; Faculty at Kalsekar Technical Campus of Mumbai University
LEAD_AUTHOR
Deepak
Panwar
dpanwar@jpr.amity.edu
2
Faculty at Dept of Computer Science at Amity School of Engineering & Technology at Amity University
AUTHOR
Abidi, A. A. (1995). Direct-conversion radio transceivers for digital communications. IEEE Journal of solid-state circuits, 30(12), 1399-1410.Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32, 909–932.
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AL‐Tabtabai, H., & Alex, A. P. (1999). Using genetic algorithms to solve optimization problems in construction. Engineering Construction and Architectural Management, 6(2), 121-132.
2
Bahai, A. R., Saltzberg, B. R., & Ergen, M. (2004). Multi-carrier digital communications: theory and applications of OFDM. Springer Science & Business Media.
3
Barhumi, I., Leus, G., & Moonen, M. (2003). Optimal training design for MIMO OFDM systems in mobile wireless channels. IEEE Transactions on signal processing, 51(6), 1615-1624.
4
Bolcskei, H., Gesbert, D., & Paulraj, A. J. (2002). On the capacity of OFDM-based spatial multiplexing systems. IEEE Transactions on communications, 50(2), 225-234.
5
Bolcskei, H., Heath, R. W., & Paulraj, A. J. (2002). Blind channel identification and equalization in OFDM-based multiantenna systems. IEEE Transactions on signal Processing, 50(1), 96-109.
6
Chung, Y.H. and S.M. Phoong, 2008. OFDM channel estimation in the presence of transmitter and receiver I/Q imbalance. Proceeding of 16th European Signal Processing Conference (EUSIPCO 2008). Lausanne, Switzerland, August 25-29, copyright by EURASIP.
7
Deneire, L., Vandenameele, P., Van Der Perre, L., Gyselinckx, B., & Engels, M. (2003). A low-complexity ML channel estimator for OFDM. IEEE Transactions on Communications, 51(2), 135-140.
8
Edfors, O., Sandell, M., Van de Beek, J. J., Wilson, S. K., & Borjesson, P. O. (1998). OFDM channel estimation by singular value decomposition. IEEE Transactions on communications, 46(7), 931-939.
9
Wan, F., Zhu, W. P., & Swamy, M. N. S. (2007, May). Linear prediction based semi-blind channel estimation for MIMO-OFDM system. In 2007 IEEE International Symposium on Circuits and Systems (pp. 3239-3242). IEEE.
10
Jeon, W. G., Paik, K. H., & Cho, Y. S. (2000, September). An efficient channel estimation technique for OFDM systems with transmitter diversity. In 11th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. PIMRC 2000. Proceedings (Cat. No. 00TH8525) (Vol. 2, pp. 1246-1250). IEEE.
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Karaa, H., Adve, R. S., & Tenenbaum, A. J. (2007, June). Linear precoding for multiuser MIMO-OFDM systems. In 2007 IEEE International Conference on Communications (pp. 2797-2802). IEEE.
12
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
13
ORIGINAL_ARTICLE
Some induced generalized Einstein aggregating operators and their application to group decision-making problem using intuitionistic fuzzy numbers
The paper aims to develop an idea of some inducing operators, namely induced intuitionistic fuzzy Einstein hybrid averaging operator, induced intuitionistic fuzzy Einstein hybrid geometric operator, induced generalized intuitionistic fuzzy Einstein hybrid averaging operator and induced generalized intuitionistic fuzzy Einstein hybrid geometric operator along with their wanted structure properties such as, monotonicity, idempotency and boundedness. The proposed operators are competent and able to reflect the complex attitudinal character of the decision maker by using order inducing variables and deliver more information to experts for decision-making. To show the legitimacy, practicality and effectiveness of the new operators, the proposed operators have been applied to decision making problems
http://aotp.fabad-ihe.ac.ir/article_113593_05b3e5f3a6d68348a75510d6d99ac66f.pdf
2020-12-01
15
49
10.22121/aotp.2020.241689.1036
I-IFEHA operator
I- IFEHG operator
I-GIFEHA operator
I-GIFEHG operator
MAGDM problem
khaista
Rahman
khaista355@yahoo.com
1
Hazara University Mansehra
LEAD_AUTHOR
Ayub
Sanam
2
Department of Mathematics and Statistics, Riphah International University, Islamabad, Pakistan
AUTHOR
Abdullah
Saleem
3
Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan
AUTHOR
Yaqub
Muhammad
4
Department of Mathematics and Statistics, Riphah International University, Islamabad, Pakistan
AUTHOR
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
1
Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96.
2
Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33(1), 37-45.
3
Garg, H. (2020). Exponential operational laws and new aggregation operators for intuitionistic multiplicative set in multiple-attribute group decision making process. Information Sciences.
4
Garg, H., Arora, R. (2019). Generalized intuitionistic fuzzy soft power aggregation operator based on t‐norm and their application in multicriteria decision‐making. International Journal of Intelligent Systems, 34(2), 215-246.
5
Garg, H., Kumar, K. (2020). A novel possibility measure to interval-valued intuitionistic fuzzy set using connection number of set pair analysis and its applications. Neural Computing and Applications, 32(8), 3337-3348.
6
Xu, Z. S. (2007). Models for multiple attribute decision making with intuitionistic fuzzy information. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(03), 285-297.
7
Yu, D., Wu, Y., Lu, T. (2012). Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making. Knowledge-Based Systems, 30, 57-66.
8
Bustince, H., Burillo, P. (1995). Correlation of interval-valued intuitionistic fuzzy sets. Fuzzy sets and systems, 74(2), 237-244.
9
Xu, Z., Xia, M. (2011). Induced generalized intuitionistic fuzzy operators. Knowledge-Based Systems, 24(2), 197-209.
10
Tan, C. (2011). Generalized intuitionistic fuzzy geometric aggregation operator and its application to multi-criteria group decision making. Soft Computing, 15(5), 867-876.
11
Xia, M., Xu, Z. (2010). Some new similarity measures for intuitionistic fuzzy values and their application in group decision making. Journal of Systems Science and Systems Engineering, 19(4), 430-452.
12
Li, D. F. (2011). The GOWA operator based approach to multiattribute decision making using intuitionistic fuzzy sets. Mathematical and Computer Modelling, 53(5-6), 1182-1196.
13
Wei, G. W. (2008). Maximizing deviation method for multiple attribute decision making in intuitionistic fuzzy setting. Knowledge-Based Systems, 21(8), 833-836.
14
Wei, G. W. (2010). GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting. Knowledge-Based Systems, 23(3), 243-247.
15
Wei, G. W. (2011). Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making. Expert systems with Applications, 38(9), 11671-11677.
16
Wei, G., Zhao, X. (2011). Minimum deviation models for multiple attribute decision making in intuitionistic fuzzy setting. International Journal of Computational Intelligence Systems, 4(2), 174-183.
17
Wei, G. W., Wang, H. J., Lin, R. (2011). Application of correlation coefficient to interval-valued intuitionistic fuzzy multiple attribute decision-making with incomplete weight information. Knowledge and Information Systems, 26(2), 337-349.
18
Grzegorzewski, P. (2004). Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric. Fuzzy sets and systems, 148(2), 319-328.
19
Yager, R. R. (2009). Some aspects of intuitionistic fuzzy sets. Fuzzy Optimization and Decision Making, 8(1), 67-90.
20
Ye, J. (2010). Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. European Journal of Operational Research, 205(1), 202-204.
21
Ye, J. (2011). Fuzzy cross entropy of interval-valued intuitionistic fuzzy sets and its optimal decision-making method based on the weights of alternatives. Expert Systems with Applications, 38(5), 6179-6183.
22
Garg, H. (2016). Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making. Computers & Industrial Engineering, 101, 53-69.
23
Garg, H. (2019). Intuitionistic fuzzy Hamacher aggregation operators with entropy weight and their applications to multicriteria decision-making problems. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, 597-613.
24
Garg, H., Kumar, K. (2019). Power geometric aggregation operators based on connection number of set pair analysis under intuitionistic fuzzy environment, Arabian Journal for Science and Engineering.
25
Garg, H., Kumar, K. (2018). A novel correlation coefficient of intuitionistic fuzzy sets based on the connection number of set pair analysis and its application. Scientia Iranica. Transaction E, Industrial Engineering, 25(4), 2373-2388.
26
Garg, H., Rani, D. (2019). Generalized Geometric Aggregation Operators Based on T-Norm Operations for Complex Intuitionistic Fuzzy Sets and Their Application to Decision-making. Cognitive Computation, 1-20.
27
Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on fuzzy systems, 15(6), 1179-1187.
28
Xu, Z., Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International journal of general systems, 35(4), 417-433.
29
Xu, Z. (2011). Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators. Knowledge-Based Systems, 24(6), 749-760.
30
Wang, W., Liu, X. (2011). Intuitionistic fuzzy geometric aggregation operators based on Einstein operations. International Journal of Intelligent Systems, 26(11), 1049-1075.
31
Wang, W., Liu, X. (2012). Intuitionistic fuzzy information aggregation using Einstein operations. IEEE Transactions on Fuzzy Systems, 20(5), 923-938.
32
Zhao, X., Wei, G. (2013). Some intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute decision making. Knowledge-Based Systems, 37, 472-479.
33
Zhao, H., Xu, Z., Ni, M., Liu, S. (2010). Generalized aggregation operators for intuitionistic fuzzy sets. International journal of intelligent systems, 25(1), 1-30.
34
Wei, G. (2010). Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making. Applied soft computing, 10(2), 423-431.
35
Yu, X., Xu, Z. (2013). Prioritized intuitionistic fuzzy aggregation operators. Information Fusion, 14(1), 108-116.
36
Su, Z. X., Xia, G. P., Chen, M. Y. (2011). Some induced intuitionistic fuzzy aggregation operators applied to multi-attribute group decision making. International Journal of General Systems, 40(8), 805-835.
37
Xu, Y., Li, Y., Wang, H. (2013). The induced intuitionistic fuzzy Einstein aggregation and its application in group decision-making. Journal of Industrial and Production Engineering, 30(1), 2-14.
38
Rahman, K., Abdullah, S., Jamil, M., Khan, M. Y. (2018). Some generalized intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute group decision making. International Journal of Fuzzy Systems, 20(5), 1567-1575.
39
Rahman, K., Ayub, S., Abdullah, S. (2020). Generalized intuitionistic fuzzy aggregation operators based on confidence levels for group decision making. Granular Computing, 1-20.
40
Jamil, M., Rahman, K., Abdullah, S., Khan, M. Y. (2020). The induced generalized interval-valued intuitionistic fuzzy einstein hybrid geometric aggregation operator and their application to group decision-making. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-16.
41
ORIGINAL_ARTICLE
An algorithm for an improved intuitionistic fuzzy correlation measure with medical diagnostic application
Correlation measure is a vital measuring operator with vast applications in decision-making. On the other hand, intuitionistic fuzzy set (IFS) is very resourceful in soft computing to tackle embedded fuzziness in decision-making. The extension of correlation measure to intuitionistic fuzzy settinghas proven to be useful in multi-criteria decision-making (MCDM). This paper introduces a new intuitionistic fuzzy correlation measure encapsulates in an algorithm by taking into account the complete parameters of IFSs. This new computing technique evaluates the strength of relationship and it is defined within the codomain of IFS. The proposed technique is demonstrated with some theoretical results, and numerically authenticated to be superior in terms of performance index in contrast to some existing correlation measures. We demonstrate the application of the new correlation measure coded with JAVA programming language in medical diagnosis to enhance efficiency since diagnosis is a delicate medical-decision-making exercise.
http://aotp.fabad-ihe.ac.ir/article_118829_8326356d34d39612658628156acd7d91.pdf
2020-12-01
51
66
10.22121/aotp.2020.249456.1041
Algorithmic approach
Correlation measure
Intuitionistic fuzzy set
Medical diagnosis
Paul
Ejegwa
ocholohi@gmail.com
1
Department of Mathematics, Statistics and Computer Science, University of Agriculture, P.M.B. 2327, Makurdi-Nigeria
LEAD_AUTHOR
Idoko
Onyeke
onyeke.idoko@uam.edu.ng
2
Department of Computer Science, University of Agriculture, P.M.B. 2373, Makurdi, Nigeria
AUTHOR
Victoria
Adah
adahvictoria14@gmail.com
3
Department of Statistics, University of Agriculture, P.M.B. 2373, Makurdi, Nigeria
AUTHOR
Atanassov, K. (1986). Intuitionistic fuzzy sets. fuzzy sets and systems 20 (1), 87-96.Baybars, I. (1986).
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Atanassov KT (1999) Intuitionistic fuzzy sets: theory and applications, Physica-Verlag, Heidelberg.
3
Atanassov KT (2012) On intuitionistic fuzzy sets theory, Springer, Berlin.
4
Boran FE, Akay D (2014) A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition. Information Sciences 255(10):45-57.
5
Chiang, D. A., & Lin, N. P. (1999). Correlation of fuzzy sets. Fuzzy sets and systems, 102(2), 221-226.
6
Davvaz, B., & Hassani Sadrabadi, E. (2016). An application of intuitionistic fuzzy sets in medicine. International journal of biomathematics, 9(03), 1650037.
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10
Ejegwa, P. A. (2020). Modified and generalized correlation coefficient between intuitionistic fuzzy sets with applications. Notes on Intuitionistic Fuzzy Sets, 26(1), 8-22.
11
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Ejegwa, P. A., Akubo, A. J., & Joshua, O. M. (2014). Intuitionistic fuzzy set and its application in career determination via normalized Euclidean distance method. European Scientific Journal, 10(15).
13
Ejegwa, P. A., & Modom, E. S. (2015). Diagnosis of viral hepatitis using new distance measure of intuitionistic fuzzy sets. Int J Fuzzy Math Arch, 8(1), 1-7.
14
Ejegwa, P. A., & Onasanya, B. O. (2019). Improved intuitionistic fuzzy composite relation and its application to medical diagnostic process. Note IFS, 25(1), 43-58.
15
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16
Garg, H. (2016). A novel correlation coefficients between Pythagorean fuzzy sets and its applications to decision‐making processes. International Journal of Intelligent Systems, 31(12), 1234-1252.
17
Garg, H. (2018). Novel correlation coefficients under the intuitionistic multiplicative environment and their applications to decision-making process. Journal of industrial & management optimization, 14(4), 1501.
18
Garg, H., & Arora, R. (2020). TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information. AIMS Mathematics, 5(4), 2944-2966.
19
Garg, H., & Kumar, K. (2018). A novel correlation coefficient of intuitionistic fuzzy sets based on the connection number of set pair analysis and its application. Scientia Iranica. Transaction E, Industrial Engineering, 25(4), 2373-2388.
20
Garg, H., & Kumar, K. (2018). A novel correlation coefficient of intuitionistic fuzzy sets based on the connection number of set pair analysis and its application. Scientia Iranica. Transaction E, Industrial Engineering, 25(4), 2373-2388.
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Hatzimichailidis, A. G., Papakostas, G. A., & Kaburlasos, V. G. (2012). A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems. International Journal of Intelligent Systems, 27(4), 396-409.
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26
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27
Liu, B., Shen, Y., Mu, L., Chen, X., & Chen, L. (2016). A new correlation measure of the intuitionistic fuzzy sets. Journal of intelligent & fuzzy systems, 30(2), 1019-1028.
28
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41
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45
ORIGINAL_ARTICLE
Policy decision making based on some averaging aggregation operators of t-spherical fuzzy sets; a multi-attribute decision making approach
Multi-attribute decision making (MADM) is a hot research area in fuzzy mathematics and to deal with that, the averaging and geometric aggregation operators (AOs) are the widely used tools. The aim of this manuscript is to propose the notion of averaging and geometric AOs in the environment of T-spherical fuzzy sets (TSFSs). TSFS enables the selection of grades of memberships from considerably a larger domain and hence overcome the drawbacks of the existing fuzzy frameworks. In this paper, we develop some novel operations for TSFSs including algebraic sum, product etc. Based on new operations some averaging AOs including T-spherical fuzzy weighted averaging (TSFWA) and T-spherical fuzzy weighted geometric (TSFWG) operators are developed. The monotonicity, idempotency and boundedness of the defined operators are investigated, and their fitness is validated using induction method. With the help of an illustrative example, the problem of policy decision making using a MADM algorithm is solved. The new proposed work and the existing literature is compared numerically and the advantages of the TSFWA and TSFWG operators are investigated over existing work.
http://aotp.fabad-ihe.ac.ir/article_119000_17252ee736989db0235a1ce04060479f.pdf
2020-12-01
69
92
10.22121/aotp.2020.241244.1035
Aggregation Operators
T-spherical fuzzy set
Picture Fuzzy Set
Multi-attribute decision making
spherical fuzzy set
Kifayat
Ullah
kifayat.phdma72@iiu.edu.pk
1
Department of Mathematics & Statistics, International Islamic University Islamabad, 44000 Islamabad, Pakistan.
AUTHOR
Tahir
Mahmood
tahirbakhat@iiu.edu.pk
2
Department of Mathematics & Statistics, International Islamic University Islamabad, 44000 Islamabad, Pakistan.
LEAD_AUTHOR
Naeem
Jan
naeem.phdma73@iiu.edu.pk
3
Department of Mathematics & Statistics, International Islamic University Islamabad, 44000 Islamabad, Pakistan.
AUTHOR
Zeeshan
Ahmad
zeeshan.msma435@iiu.edu.pk
4
Department of Mathematics & Statistics, International Islamic University Islamabad, 44000 Islamabad, Pakistan.
AUTHOR
Zadeh, L.A., Information and control. Fuzzy sets, 1965. 8(3): p. 338-353.
1
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.
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Atanassov, K. T. (1999). Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets (pp. 1-137). Physica, Heidelberg.
3
Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33(1), 37-45.
4
Atanassov, K. and G. Gargov, Interval valued intuitionistic fuzzy sets. Fuzzy sets and systems, 1989. 31(3): p. 343-349.
5
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529-539.
6
Torra, V., & Narukawa, Y. (2009, August). On hesitant fuzzy sets and decision. In 2009 IEEE International Conference on Fuzzy Systems (pp. 1378-1382). IEEE.
7
Yager, R. R. (2013, June). Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE.
8
Rahman, K., Khan, M. A., Ullah, M., & Fahmi, A. (2017). Multiple attribute group decision making for plant location selection with Pythagorean fuzzy weighted geometric aggregation operator. The Nucleus, 54(1), 66-74.
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12
Garg, H. (2017). Some picture fuzzy aggregation operators and their applications to multicriteria decision-making. Arabian Journal for Science and Engineering, 42(12), 5275-5290.
13
Wei, G. (2016). Picture fuzzy cross-entropy for multiple attribute decision making problems. Journal of Business Economics and Management, 17(4), 491-502.
14
Wei, G. (2017). Picture fuzzy aggregation operators and their application to multiple attribute decision making. Journal of Intelligent & Fuzzy Systems, 33(2), 713-724.
15
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16
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17
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18
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19
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20
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21
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22
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26
Pei, Z., & Zheng, L. (2012). A novel approach to multi-attribute decision making based on intuitionistic fuzzy sets. Expert Systems with Applications, 39(3), 2560-2566.
27
Liu, H. W., & Wang, G. J. (2007). Multi-criteria decision-making methods based on intuitionistic fuzzy sets. European Journal of Operational Research, 179(1), 220-233.
28
Ye, J. (2009). Multicriteria fuzzy decision-making method based on a novel accuracy function under interval-valued intuitionistic fuzzy environment. Expert systems with Applications, 36(3), 6899-6902.
29
Xu, Z. (2011). Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators. Knowledge-Based Systems, 24(6), 749-760.
30
Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on fuzzy systems, 15(6), 1179-1187.
31
Garg, H., & Rani, D. (2019). Novel aggregation operators and ranking method for complex intuitionistic fuzzy sets and their applications to decision-making process. Artificial Intelligence Review, 1-26.
32
Edalatpanah, S. A. (2020). Neutrosophic structured element. Expert systems, 37(5), e12542.
33
Garg, H. (2016). A new generalized Pythagorean fuzzy information aggregation using Einstein operations and its application to decision making. International Journal of Intelligent Systems, 31(9), 886-920.
34
Garg, H., Rani, M., Sharma, S. P., & Vishwakarma, Y. (2014). Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment. Expert Systems with Applications, 41(7), 3157-3167.
35
Garg, H. (2016). A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem. Journal of Intelligent & Fuzzy Systems, 31(1), 529-540.
36
Garg, H. (2016). Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making. Computers & Industrial Engineering, 101, 53-69.
37
Garg, H. (2016). A novel correlation coefficients between Pythagorean fuzzy sets and its applications to decision‐making processes. International Journal of Intelligent Systems, 31(12), 1234-1252.
38
Garg, H. (2016). Some series of intuitionistic fuzzy interactive averaging aggregation operators. SpringerPlus, 5(1), 1-27.
39
Liao, H., & Xu, Z. (2014). Some new hybrid weighted aggregation operators under hesitant fuzzy multi-criteria decision making environment. Journal of Intelligent & Fuzzy Systems, 26(4), 1601-1617.
40
Xia, M., Xu, Z., & Chen, N. (2013). Some hesitant fuzzy aggregation operators with their application in group decision making. Group Decision and Negotiation, 22(2), 259-279.
41
Ali, Z., & Mahmood, T. (2020). Maclaurin symmetric mean operators and their applications in the environment of complex q-rung orthopair fuzzy sets. Computational and Applied Mathematics, 39, 1-27.
42
Peng, X., & Yang, Y. (2015). Some results for Pythagorean fuzzy sets. International Journal of Intelligent Systems, 30(11), 1133-1160.
43
Garg, H. (2020). Exponential operational laws and new aggregation operators for intuitionistic multiplicative set in multiple-attribute group decision making process. Information Sciences, 538, 245-272.
44
Gupta, S., Garg, H., & Chaudhary, S. (2020). Parameter estimation and optimization of multi-objective capacitated stochastic transportation problem for gamma distribution. Complex & Intelligent Systems, 6(3), 651-667.
45
Garg, H. (2020). New ranking method for normal intuitionistic sets under crisp, interval environments and its applications to multiple attribute decision making process. Complex & Intelligent Systems, 6, 559-571.
46
Munir, M., Kalsoom, H., Ullah, K., Mahmood, T., & Chu, Y. M. (2020). T-spherical fuzzy Einstein hybrid aggregation operators and their applications in multi-attribute decision making problems. Symmetry, 12(3), 365.
47
Liu, P., Mahmood, T., & Ali, Z. (2020). Complex q-rung orthopair fuzzy aggregation operators and their applications in multi-attribute group decision making. Information, 11(1), 5.
48
Ullah, K., Mahmood, T., Ali, Z., & Jan, N. (2020). On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition. Complex & Intelligent Systems, 6(1), 15-27.
49
Ullah, K., Mahmood, T., & Garg, H. (2020). Evaluation of the performance of search and rescue robots using T-spherical fuzzy hamacher aggregation operators. International Journal of Fuzzy Systems, 22(2), 570-582.
50
Mahmood, T., Ullah, K., & Khan, Q. (2018). Some aggregation operators for bipolar-valued hesitant fuzzy information. Infinite Study.
51
Mao, X., Guoxi, Z., Fallah, M., & Edalatpanah, S. A. (2020). A Neutrosophic-Based Approach in Data Envelopment Analysis with Undesirable Outputs. Mathematical Problems in Engineering, 2020.
52
Ali, Z., Mahmood, T., & Yang, M. S. (2020). TOPSIS Method Based on Complex Spherical Fuzzy Sets with Bonferroni Mean Operators. Mathematics, 8(10), 1739.
53
Ali, Z., Mahmood, T., & Yang, M. S. (2020). Complex T-spherical fuzzy aggregation operators with application to multi-attribute decision making. Symmetry, 12(8), 1311.
54
Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255-263.
55
Yang, W., Cai, L., Edalatpanah, S. A., & Smarandache, F. (2020). Triangular single valued neutrosophic data envelopment analysis: application to hospital performance measurement. Symmetry, 12(4), 588.
56
ORIGINAL_ARTICLE
Computation of three-stage stochastic transportation planning under an uncertain environment
Stochastic programming is often used to solve optimization problems where parameters are uncertain. In this article, we have proposed a mathematical model for a three-stage transportation problem, where the parameters, namely transport costs, demand, unload capacity and external purchasing costs are uncertain. In order to remove the uncertainty, we have proposed a new transformation technique to reformulate the uncertain model deterministically with the help of Essen inequality. The obtained equivalent deterministic model is nonlinear. Furthermore, we have provided a theorem to ensure that the deterministic model gives a feasible solution. Finally, a numerical example, following uniform random variables, is presented to illustrate the model and methodology.
http://aotp.fabad-ihe.ac.ir/article_119248_3f54634045711497ac70b96bb5d15e54.pdf
2020-12-01
93
115
10.22121/aotp.2020.252845.1047
stochastic optimization
Chance constraints programming
Essen inequality
Shubham
Singh
shubhamsingh2150@gmail.com
1
iit kharagpur
LEAD_AUTHOR
Ritu
Nigam
ritunigamiitkgp@gmail.com
2
IIT Kharagpur
AUTHOR
Debjani
Chakraborty
debjani@maths.iitkgp.ac.in
3
Department of Mathematics IIT Kharagpur
AUTHOR
Atalay, K. D., & Apaydin, A. (2011). Gamma distribution approach in chance-constrained stochastic programming model. Journal of Inequalities and Applications, 2011(1), 1-13.
1
Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. Springer Science & Business Media.
2
Charnes A, Cooper W.W. (1959). Chance-constrained programming. Managementscience. 6(1), pp. 73-79.
3
Hulsurkar, S., Biswal, M. P., & Sinha, S. B. (1997). Fuzzy programming approach to multi-objective stochastic linear programming problems. Fuzzy Sets and Systems, 88(2), 173-181.
4
Jiménez, F., & Verdegay, J. L. (1998). Uncertain solid transportation problems. Fuzzy sets and systems, 100(1-3), 45-57.
5
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions, volume 2 (Vol. 289). John wiley & sons.
6
Liu, B., Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), pp. 227-237.
7
Olson, D. L., Swenseth, S. R. (1987). A linear approximation for chance-constrained programming. Journal of the Operational Research Society, 38(3), pp. 261-267.
8
Petrov, V. V. (1995). Limit theorems of probability theory: sequences of independent random variables.
9
Prékopa, A. (2013). Stochastic programming (Vol. 324). Springer Science & Business Media.
10
Roy, S. K. (2014). Multi-choice stochastic transportation problem involving Weibull distribution. International Journal of Operational Research, 21(1), 38-58.
11
Roy, S. K., & Midya, S. (2019). Multi-objective fixed-charge solid transportation problem with product blending under intuitionistic fuzzy environment. Applied Intelligence, 49(10), 3524-3538.
12
Roy, S. K., Mahapatra, D. R., & Biswal, M. P. (2012). Multi-choice stochastic transportation problem with exponential distribution. Journal of Uncertain Systems, 6(3), 200-213.
13
Ruszczyński, A., & Shapiro, A. (2003). Stochastic programming models. Handbooks in operations research and management science, 10, 1-64.
14
Sagratella, S., Schmidt, M., & Sudermann-Merx, N. (2020). The noncooperative fixed charge transportation problem. European Journal of Operational Research, 284(1), 373-382.
15
Shen, J., & Zhu, K. (2020). An uncertain two-echelon fixed charge transportation problem. Soft Computing, 24(5), 3529-3541.
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Singh, S., Pradhan, A., & Biswal, M. P. (2019). Multi-objective solid transportation problem under stochastic environment. Sādhanā, 44(5), 105.
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Symonds, G. H. (1967). Deterministic solutions for a class of chance-constrained programming problems. Operations Research, 15(3), 495-512.
18
van Beek, P. (1972). An application of Fourier methods to the problem of sharpening the Berry-Esseen inequality. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete, 23(3), 187-196.
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Wang, X., & Ning, Y. (2017). Uncertain chance-constrained programming model for project scheduling problem. Journal of the operational research society, 1-9.
20
Williams, A. C. (1963). A stochastic transportation problem. Operations Research, 11(5), 759-770.
21
Yang, L., & Liu, L. (2007). Fuzzy fixed charge solid transportation problem and algorithm. Applied soft computing, 7(3), 879-889.
22
ORIGINAL_ARTICLE
Picture fuzzy labelling graphs with an application
The main objective of this paper is to introduce the idea of picture fuzzy labelling of graphs and the concepts of strong arc, partial cut node, bridge of picture fuzzy labelling graphs, picture fuzzy labelling tree and cycle along with their properties and results. In addition, an application of the picture fuzzy graph labelling model for the human circulatory system has been discussed.
http://aotp.fabad-ihe.ac.ir/article_120594_4897d46500d41fdab17af0144ea5a819.pdf
2020-12-01
117
134
10.22121/aotp.2020.256899.1053
Picture fuzzy graph labelling
strength of connectedness
picture fuzzy labelling tree
picture fuzzy labelling cycle
fuzzy labelling graphs
Ajay
Devaraj
dajaypravin@gmail.com
1
Department of Mathematics, Sacred Heart College (Autonomous), Tirupattur, Tamilnadu
LEAD_AUTHOR
Chellamani
P
joshmani238@gmail.com
2
Ph. D Research Scholar, PG and Research Department of Mathematics, Sacred Heart College (Autonomous), Tirupattur – 635 601, Tamil Nadu
AUTHOR
Ajay, D., & Aldring, J. (2019). A Decision Making Technique Based on Similarity Measure and Entropy of Bipolar Neutrosophic Sets. The International journal of analytical and experimental modal analysis, 11(9), 520-529.
1
Ajay, D., Aldring, J., Seles Martina, D. J., & Abirami, S. A. (2020). A SVTrN-number approach of multi-objective optimisation on the basis of simple ratio analysis based on MCDM method. International Journal of Neutrosophic Science, 5(1), 16-28.
2
Ajay, D., and Chellamani, P. (2020). Fuzzy magic labelling of Neutrosophic path and star graph, Advances in Mathematics: Scientific Journal, 9(8), p.6059–6070.
3
Ajay, D., Broumi, S., & Aldring, J. (2020). An MCDM method under neutrosophic cubic fuzzy sets with geometric bonferroni mean operator. Neutrosophic Sets and Systems, 32, 187-202.
4
Ajay, D., Charisma, J. J., & Chellamani, P. (2019). Fuzzy Magic and Bi-magic Labelling of Intuitionistic Path Graph. International Journal of Recent Technology and Engineering, 8(4), 11508-11512.
5
Ajay, D., Manivel, M., & Aldring, J. (2019). Neutrosophic Fuzzy SAW Method and It’s Application. The International journal of analytical and experimental modal analysis, 11, 881-887.
6
Atanassov, K.T. (1986); Intuitionstic fuzzy sets, Fuzzy sets and systemsVol. 20(1), pp.87-96.
7
Cường, B. C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409.
8
Cuong, B. C., & Kreinovich, V. (2013, December). Picture Fuzzy Sets-a new concept for computational intelligence problems. In 2013 third world congress on information and communication technologies (WICT 2013) (pp. 1-6). IEEE.
9
Cuong, B. C., Huyen, P. T., Van Chien, P., & Van Hai, P. (2019, October). Some Fuzzy Inference Processes in Picture Fuzzy Systems. In 2019 11th International Conference on Knowledge and Systems Engineering (KSE) (pp. 1-5). IEEE.
10
Deli, I., & Öztürk, E. K. (2020). Two Centroid Point for SVTN-Numbers and SVTrN-Numbers: SVN-MADM Method. In Neutrosophic Graph Theory and Algorithms (pp. 279-307). IGI Global.
11
Deli, I., & Şubaş, Y. (2017). Some weighted geometric operators with SVTrN-numbers and their application to multi-criteria decision making problems. Journal of Intelligent & Fuzzy Systems, 32(1), 291-301.
12
Kaufmann, A. (1975). Theory of fuzzy subsets: introduction to the. Fundamental theoretical elements. Academic Press.
13
Nagoor Gani, A., & Akram, M. (2014). Novel properties of fuzzy labeling graphs. Journal of Mathematics, 2014.
14
Rosa, A. (1966, July). Theory of graphs. In International Symposium, Rome, July (pp. 349-355).
15
Rosenfeld, A. (1975). Fuzzy graphs. In Fuzzy sets and their applications to cognitive and decision processes (pp. 77-95). Academic press.
16
Smarandache, F., A (1999).Unifying Field in Logics, Neutrosophic Logic: Neutrosophy, Neutrosophic Set, Neutrosophic Probability, Rehoboth: American Research Press.
17
Zadeh, L.A., (1965) Fuzzy sets, Information and Control.; Vol. 8, pp. 338-353.
18
Zuo, C., Pal, A., & Dey, A. (2019). New concepts of picture fuzzy graphs with application. Mathematics, 7(5), 470.
19
ORIGINAL_ARTICLE
A new type of open set and its applications
In this paper we introduce some new separation axioms by utilizing the notions of alphaomega-p-open sets and alphaomega-pre closure operator and the implication between the existing spaces are provided. Also as an application, we study some continuous functions and graph functions using this separation axioms. Basic theorems and properties are also investigated.
http://aotp.fabad-ihe.ac.ir/article_120595_fa415ec0bd117dc1b7f259a6cd825264.pdf
2020-12-01
135
154
10.22121/aotp.2020.257115.1054
sober space
separation axioms
continuous function
graph function
Parimala
Mani
rishwanthpari@gmail.com
1
Department of Mathematics Bannari Amman Institute of Technology
LEAD_AUTHOR
Cenap
Ozel
cenap.ozel@gmail.com
2
King Abdulaziz University
AUTHOR
Karthika
Muthusamy
karthikamuthusamy1991@gmail.com
3
Department of Mathematics Bannari Amman Institute of Technology
AUTHOR
Bhattacharyya, P. (1987). Semi-generalized closed sets in topology. Indian J. Math., 29(3), 375-382.
1
Caldas, M. (2001). A separation axiom between pre-T0 and pre-T1. East West Math, 3(02), 171-177.
2
Caldas, M., Fukutake, T., Jafari, S., & Noiri, T. (2005). Some applications of delta-preopen sets in topological spaces. BULLETIN-INSTITUTE OF MATHEMATICS ACADEMIA SINICA, 33(3), 261.
3
Jafari, S. (2001). On a weak separation axiom. Far East Journal of Mathematical Sciences, 3(5), 779-788.
4
Jafari, S. (2006). On certain types of notions via preopen sets. Tamkang Journal of Mathematics, 37(4), 391-398.
5
Jafari, S. A. E. I. D. (2000). Pre-rarely p-continuous functions between topological spaces. Far East J. Math. Sci. Special, 2000(Part I), 87-96.
6
John, M. S. (2000). On w-closed sets in Topology. Acta Ciencia Indica, 4, 389-392.
7
Kar, A., & Bhattacharyya, P. (1990). Some weak separation axioms. Bull. Calcutta Math. Soc, 82(5), 415-422.
8
Kumar, M. V. (2001). ̂g-locally closed sets and ̂GLC-functions. Indian Journal of Mathematics, 43(2), 231-248.
9
Levine, N. (1963). Semi-open sets and semi-continuity in topological spaces. The American mathematical monthly, 70(1), 36-41.
10
M. Parimala , R. Udhayakumar , R. Jeevitha , V. Biju,(2017). On -closed sets in topological spaces, International Journal of Pure and Applied Mathematics, 115(5), , 1049-1056.
11
Mashhour, A. S. (1982). On preconlinuous and weak precontinuous mappings. In Proc. Math. Phys. Soc. Egypt. (Vol. 53, pp. 47-53).
12
Njȧstad, O. (1965). On some classes of nearly open sets. Pacific journal of mathematics, 15(3), 961-970.
13
Noiri, T. (1984). Supercontinuity and some strong forms of continuity. Indian J. pure appl. Math, 15(3), 241-250.
14
Nour, T. M. (1989). Contributions to the theory of bitopological spaces. Ph. D. Thesis, Delhi University, India.
15
Parimala, M., Karthika, M., Smarandache, F., & Broumi, S. (2020). On closed sets and its connectedness in terms of neutrosophic topological spaces. Infinite Study.
16
Parimala, M., Ozel, C., & Udhayakumar, R. (2018). On ultra separation axioms via αω-open sets. In Advances in Algebra and Analysis (pp. 97-102). Birkhäuser, Cham.
17
Raychaudhuri, S., & Mukherjee, M. N. (1993). On δ-almost continuity and δ-preopen sets. Bull. Inst. Math. Acad. Sinica, 21(4), 357-366.
18
Veera Kumar, M. K. R. S. (2000). Between semi-closed sets and semi-pre-closed set.
19
Velicko, N. V. (1968). H-closed topological spaces. Amer. Math. Soc. Transl., 78, 103-118.
20
Willard, S. (1970). General Topology, Acddison-Wesley. Reading Mas sachusetts.
21
ORIGINAL_ARTICLE
Alternative approach to find optimal solution of assignment problem using Hungarian method by trapezoidal intuitionistic type-2 fuzzy data
Now a day’s uncertainty is a common thing in science and technology. It is undesirable also. Based on alternative view, it should be avoided by all possible means. Based on modern view uncertainty is considered essential to science and technology, it is not only the unavoidable plague but also it has impacted a great utility. Fuzzy set theory mainly developed based on inexactness, vagueness, relativity etc. fuzzy set may be used in mathematical modelling in every scientific discipline. It can also use for improving the generality of analytical solution. It has many uses in various streams like -operation research, control theory differential equations, fuzzy system reliability, optimization and management sciences etc. In this paper we first describe Trapezoidal intuitionistic Type 2 fuzzy number(TrIT2FN) with arithmetic operations and solve an assignment problem using Hungarian method for Trapezoidal intuitionistic Type 2 fuzzy number (TrIT2FN).
http://aotp.fabad-ihe.ac.ir/article_120828_fcedd08d9a3d5484f1922d9434b48255.pdf
2020-12-01
155
173
10.22121/aotp.2020.257124.1055
Fuzzy set
Trapezoidal intuitionistic Type 2 fuzzy number (TrIT2FN)
Hungarian method
Assignment algorithm
Rahul
Kar
rkar997@gmail.com
1
Department of Mathematics, Springdale High School, Kalyani, West Bengal
LEAD_AUTHOR
Ashok
Shaw
deanacademic@regent.ac.in
2
Dean Academic, Department of Mathematics, Regent Education and Research Foundation Bara Kanthalia, Sewli Telini Para, North 24 Parganas, Barrackpore, Kolkata, West Bengal 700121
AUTHOR
Bappa
Das
bappa.das1@gmail.com
3
Department of Mathematics, BANKURA UNIVERSITY, West Bengal, India
AUTHOR
Aliev, R. A., Pedrycz, W., Guirimov, B. G., Aliev, R. R., Ilhan, U., Babagil, M., & Mammadli, S. (2011). Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization. Information Sciences, 181(9), 1591-1608.
1
Baguley, P., Page, T., Koliza, V., & Maropoulos, P. (2006). Time to market prediction using type‐2 fuzzy sets. Journal of Manufacturing Technology Management.
2
Castillo, O., & Melin, P. (2008). Intelligent systems with interval type-2 fuzzy logic. International Journal of Innovative Computing, Information and Control, 4(4), 771-783.
3
Castillo, O., Huesca, G., & Valdez, F. (2005, June). Evolutionary computing for optimizing type-2 fuzzy systems in intelligent control of non-linear dynamic plants. In NAFIPS 2005-2005 Annual Meeting of the North American Fuzzy Information Processing Society (pp. 247-251). IEEE.
4
Chhibber, D., Bisht, D. C., & Srivastava, P. K. (2019, January). Ranking approach based on incenter in triangle of centroids to solve type-1 and type-2 fuzzy transportation problem. In AIP Conference Proceedings (Vol. 2061, No. 1, p. 020022). AIP Publishing LLC.
5
Fisher, P. F. (2010). Remote sensing of land cover classes as type 2 fuzzy sets. Remote Sensing of Environment, 114(2), 309-321.
6
Hu, J., Zhang, Y., Chen, X., & Liu, Y. (2013). Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number. Knowledge-Based Systems, 43, 21-29.
7
John, R., & Coupland, S. (2007). Type-2 fuzzy logic: A historical view. IEEE computational intelligence magazine, 2(1), 57-62.
8
Kar, R., & Shaw, A. K. (2018). Some Arithmetic Operations on Trapezoidal Fuzzy Numbers and its Application in Solving Linear Programming Problem by Simplex Algorithm. International Journal of Bioinformatics and Biological Sciences, 6(2), 77-86.
9
Kar, R., & Shaw, A. K. (2019). Some Arithmetic Operations on Triangular Intuitionistic Fuzzy Number and its Application in Solving Linear Programming Problem by Simplex Algorithm. International Journal of Bioinformatics and Biological Sciences, 7(1/2), 21-28.
10
Karnik, N. N., & Mendel, J. M. (2001). Operations on type-2 fuzzy sets. Fuzzy sets and systems, 122(2), 327-348.
11
Khalil, A. M., & Hassan, N. (2017). A note on “A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets”. Applied Mathematical Modelling, 41, 684-690.
12
Kumar, P. S. (2020). Intuitionistic fuzzy zero point method for solving type-2 intuitionistic fuzzy transportation problem. International Journal of Operational Research, 37(3), 418-451.
13
Lee, C. H., Hong, J. L., Lin, Y. C., & Lai, W. Y. (2003). Type-2 fuzzy neural network systems and learning. International Journal of Computational Cognition, 1(4), 79-90.
14
Liang, Q., & Mendel, J. M. (2000). Interval type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy systems, 8(5), 535-550.
15
Mazandarani, M., & Najariyan, M. (2014). Differentiability of type-2 fuzzy number-valued functions. Communications in Nonlinear Science and Numerical Simulation, 19(3), 710-725.
16
Mendel, J. M., & John, R. B. (2002). Type-2 fuzzy sets made simple. IEEE Transactions on fuzzy systems, 10(2), 117-127.
17
Nagarajan, D., Lathamaheswari, M., Sujatha, R., & Kavikumar, J. (2018). Edge detection on DICOM image using triangular norms in type-2 fuzzy. International Journal of Advanced Computer Science and Applications, 9(11), 462-475.
18
Sinha, B., Das, A., & Bera, U. K. (2016). Profit maximization solid transportation problem with trapezoidal interval type-2 fuzzy numbers. International Journal of Applied and Computational Mathematics, 2(1), 41-56.
19
Starczewski, J. T. (2009). Efficient triangular type-2 fuzzy logic systems. International journal of approximate reasoning, 50(5), 799-811.
20
Tao.Z, Jian.X “Type-2 intuitionistic fuzzy sets”; Control Theory & Applications.
21
Wang, J. Q., Yu, S. M., Wang, J., Chen, Q. H., Zhang, H. Y., & Chen, X. H. (2015). An interval type-2 fuzzy number based approach for multi-criteria group decision-making problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 23(04), 565-588.
22
Wu, D., & Tan, W. W. (2004, July). A type-2 fuzzy logic controller for the liquid-level process. In 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 04CH37542) (Vol. 2, pp. 953-958). IEEE.
23